On the other hand, activation-behavior correlations may not be found for tasks in which both sustained and selective attention contribute variance to the final outcome, as in the case of our two selective attention studies [4].
This procedure offers a test of whether feature-based attention shares independent variance with WMC after having accounted for the possible shared variance between spatial attention and WMC.
In fact, after having accounted for the effect of spatial attention, the shared variance between WMC and feature-based attention is still 12.7% (r =.356²).
It is well-known that downside risk measures such as TCE and CVaR only characterize the tail expectation, and pay no attention to the tail variance beyond the VaR.
It is important however, to draw attention to the high variance in the data.
This study showed that especially divided attention contributes to the variance, which has potential relevance for future intervention studies.
ANOVA, analysis of variance; AP, attention-placebo; CBT, cognitive behavior therapy; FIQ, Fibromyalgia Impact Questionnaire; f/u, follow-up; MPI, Multidimensional Pain Inventory; ns, not significant; OBT, operant behaviour therapy; SD, standard deviation.
For the random load main attention is paid to the variance of vibration at the observation point.
Computer usage behaviour and demographics accounted for 12.4% of the variance in sustained attention, with a significant 11.6% uniquely predicted by computer usage behaviour.
Confirmatory factor models were applied to decompose the variance of the attention data into three components: executive control due to stimulus manipulation, automatic learning as the result of extended practice, and a general component independent of any changes.
